Extending Dynamic Range of Phase Diversity

A method for extending dynamic range of phase diversity (PD) is proposed. The estimating of pupil phase is divided into two steps. Low-order Zernike coefficients of the phase are estimated firstly before all of the Zernike coefficients estimated. The method is based on the assumption that the aberrations of the system are dominated by low order Zernike modes, which is true to most practical situations. The algorithm of Broyden-Fletcher-Goldfarb-Shanno(BFGS) is used in the two steps for optimizing process. Computer simulations are conducted after the theoretical analysis. The result shows that this method can effectively avoid the trap in local minimum during iteration process, and the dynamic range that PD can detect extends from about 2λ to about 4λ.     

非线性部分最小二乘方法的研究与应用

该文将部分最小二乘算法与径向基函数神经网络相结合,给出了一种非线性部分最小二乘建模方法,可以更加有效地处理过程非线性和数据共线性等复杂特性,提高模型的精度和推广能力。该方法在确定径向基函数神经网络的隐节点时,采用了一种改进型的k-均值聚类算法来自动确定最优的聚类区数。对煤气化炉合成气组分浓度软测量建模的应用结果表明,采用该方法建立的软测量模型在模型精度和推广能力等方面明显优于二次型多项式部分最小二乘方法建立的模型,并且计算精度满足工业生产的实际要求。     

基于特征根比分布的协作频谱感知算法研究

介绍两种基于大维随机矩阵理论(Random Matrix Theory,RMT)的频谱感知方法,综合对比讨论并验证其感知性能。这些方法以采样协方差矩阵的最大最小特征根之比作为判决统计量与相应的判决门限相比较来确定频谱是否空闲。通过与能量检测(Energy Detection,ED)算法仿真对比,它们克服了能量检测中需要预知噪声信息的缺点,并具有不受噪声不确定度影响的优点,有较高检测性能和优越性。     

基于公众通信网络平台的精确农业网络构成

为提高信息化程度,构建精确农业网络,利用现代公众通信网络平台,结合无线传感器网络,配置网络拓扑,设计Sink节点802.15.4协议与GSM网络协议转换的适配器和协议集,可低成本便捷地实现多个农业生产场景综合监测和调控,使信息技术与农业生产有效融合。     

遥感图像格式的分析与应用

分析了遥感数字图像格式,遥感图像的TIFF文件格式。分离出遥感图像TIFF文件中的标记(tag),对这些标记加以分析,还原出遥感图像,从而对该遥感图像进行各种处理,为后续的遥感图像挖掘准备数据。     

遥感技术应用于河流对城市气候影响研究

为了确定松花江水域冬夏季对哈尔滨市及周边区域的影响,给城市河流规划与合理利用提供正确的理论支持,采用地面数据采集与遥感数据采集相结合的方法,对2004年1月25日和6月1日的热红外遥感影像图进行分析,以流经哈尔滨市的松花江段为研究对象,根据冬夏季温度、相对湿度分布,确定松花江城市河道对周边区域的影响.研究结果表明:在夏季,市区温度较郊区温度低,水域两侧地表温度呈递增分布,且变化趋势北岸比南岸明显,湿度市区较郊区高,市区以松花江及毗邻地区为湿度分布中心,且北岸比南岸影响半径大.在冬季,市区温度比郊区温度高约4℃,水域两侧温度分布差异明显,冰封水体表面温度与南岸温度接近,比北岸温度高约2℃,湿度分布符合城市干岛效应,市区湿度比郊区低约1%.该成果为后期地面定点观测和移动测量奠定了数据基础与技术支撑.     

Emergency aero-photo survey after the 5.12 Wenchuan Earthquake,China

After the 5.12 Wenchuan Earthquake which took place on May 12, 2008, aerial remote sensing has rapidly covered all the influence sites of the earthquake, emergency aero-photo interpretation has revealed the disaster distribution limitation, and captured the information of location, dimension of the destroyed houses, roads and other structures, blocked rivers, etc, which provided destroying conditions of the time for rescuing lives, rebuilding traffic lines and estimating disaster situation. The further interpretation and analysis indicate that large scale second growth geological disasters mainly distribute in the distance of 0–300 km and 45°–50° orientation from the epicenter. About 137 latent large-scale landslides and debris-flows will occur in this rain season and coming several years, so how to avoid current geological disasters as well as the latent large disasters should be considered in making rebuilding and developing plan.     

Image decomposing for inpainting using compressed sensing in DCT domain

Inpainting images with occlusion or corruption is a challenging task. Most existing algorithms are pixel based, which construct a statistical model from image features. However, in these algorithms, the frequency component is not sufficiently addressed. In this paper, we propose a novel algorithm that utilizes compressed sensing （CS） in frequency domain to reconstruct corrupted images. In order to reconstruct image, we first decompose the image into two functions with different basic characteristics - structure component and textual component. We seek a sparse representation for the functions and use the DCT coefficients of this representation to generate an over-complete dictionary. Experimental results on real world datasets demonstrate the efficacy of our method in image inpainting. We compare our method with three state-of-the-art inpalnting algorithms and demonstrate its advantages in terms of both quantitative and qualitative aspects.     

Variational PCA fusion for resolution Pan-sharpening very high imagery

The Pan-sharpening approach based on principle component analysis （PCA） is affected by severe spectral distortion. To address this problem, a new pan-sharpening model based on PCA and variational technique is proposed to construct the substitute image of the first principal component （PC1）. The energy functional consists of three terms. The first term injects PC1 with the geometric structure of the panchromatic （Pan） image. The second term preserves the spectral pattern of the multi-spectral image in the merged result. And the third term guarantees the smoothness of the functional optimization solution. The fusion result is given by the minimum of the energy functional, which is computed with the gradient descend flow. The experiments on QuickBird and IKONOS datasets validate the effectiveness of the proposed model. Compared with the state- of-the-art pan-sharpening approaches, this model exhibits a better trade-off between improving spatial quality and preserving spectral signature of the MS image.     

Joint optimal sensing time and power allocation for multi-channel cognitive radio networks considering sensing-channel selection

In this paper,we consider a multi-channel cognitive radio network(CRN)where each secondary user(SU)can only choose to sense a subset of channels.We formulate a joint optimization problem of sensingchannel selection,sensing time and power allocation under the constraints of average transmit power budget and average interference power budget,which maximizes the CRN’s total throughput.We propose a greedy algorithm to solve the joint optimization problem,which has much less computational complexity.Moreover,it is shown that the search space of the greedy algorithm can be further pruned.Finally,numerical results demonstrate that the greedy algorithm has comparable performance to the exhaustive search algorithm.